Smoking is a major cause of health disparities, and socioeconomic status (SES) is strongly associated with lower rates of smoking cessation. Several major conceptual models have been proposed that share a key, common mechanism linking SES to health behaviors such as cessation, noting that the life circumstances associated with low SES lead to greater exposure to stress, which then influences behavior. Unfortunately, the search for effective policies and interventions to reduce smoking among low SES individuals is severely hampered by the paucity of research on the mechanisms underlying cessation in this population. This longitudinal cohort study will examine the influence of SES and social history, contextual and environmental influences, biobehavioral/psychosocial predispositions, and acute momentary precipitants on stress, smoking lapse, and abstinence among 300 smokers attempting to quit. This study is guided by an overarching conceptual framework derived from models of the social determinants of health, social cognitive theories, and prior empirical findings. Participants will be assessed using real-time, field-based, state of the science methodologies consisting of Autosense, ecological momentary assessment (EMA), and geographic positioning system (GPS). Autosense tracks behavioral and physiologic data in real-time and can objectively detect when an individual smokes or encounters a stressor. GPS permits real-time spatial mapping of location patterns, which can be paired with EMA and Autosense data, and with relevant environmental exposures/characteristics (e.g., tobacco outlet exposure; area-level poverty) using geographic information system data. Principal outcomes of interest are lapse and stress ascertained in real time through Autosense, and early and long-term abstinence from smoking. This research would be the first to ever combine objective and dynamic indices of smoking lapse, stress, and key environmental influences in the study of smoking cessation. The comprehensive, multi-method approach is a major advance for the field as it eliminates problems related to an exclusive reliance on self-report for key outcomes and predictors. In addition, this is the first study to include empirically based machine learning approaches to fully mine the voluminous body of data yielded by real time assessment approaches, and to include the framework of dynamic prediction models, a novel statistical approach. The results will inform the tailoring of policies and interventions targeted at reducing the profound smoking-related disparities experienced by low SES individuals.